Separate Input Research Articles

Physiological and morphological traits affect contemporary range expansion and implications for species distribution modelling in an amphibian species.

Species range shifts due to climate alterations have been increasingly well-documented. Although amphibians are one of the most sensitive groups of animals to environmental perturbations due to climate change, almost no studies have offered evidence of poleward distribution shifts in this taxon in response to climate warming. Range shifts would be facilitated by variation in traits associated with the ability of species to persist and/or shift their range in the face of climate change, but the extent and consequences of intraspecific variation in these traits is unclear. We studied the role of intraspecific variation in the ongoing range shift of green treefrogs (Hyla cinerea) in response to climate change. We explored factors that are often associated with range shifts to test the hypothesis that there are differences in these traits between recently range-expanded and nearby historical populations. We then tested the consequences of intraspecific variation for modelling climate-induced range shifts by comparing species distribution models (SDMs) that used as input either data from the entire species range or separate inputs from 'subpopulations' corresponding to the historical range or the recently expanded range. We expected that building a separate SDM for each population would more accurately characterize the species range if historical and expanded populations differed in traits related to their response to climate. We found that critical thermal minimum decreased and thermal breadth increased with latitude, but the effect of latitude was significantly stronger for expanded populations compared to historical populations. Additionally, we found that individuals from expanded populations had longer leg lengths when compared to their historical counterparts. Finally, we found higher model accuracy for one of the population-level SDMs than the species-level SDM. Our results suggest that thermal tolerance and dispersal morphologies are associated with amphibian distributional shifts as these characteristics appear to facilitate rapid range expansion of a native anuran. Additionally, our modelling results emphasize that SDM accuracy could be improved by dividing a species range to consider potential differences in traits associated with climate responses. Future research should identify the mechanisms underlying intraspecific variation along climate gradients to continue improving SDM prediction of range shifts under climate change.

Avoid backtracking and burn your inputs: CONUS-scale watershed delineation using OpenMP

The Memory-Efficient Watershed Delineation (MESHED) parallel algorithm is introduced for Contiguous United States (CONUS)-scale hydrologic modeling. Delineating tens of thousands of watersheds for a continental-scale study can not only be computationally intensive, but also be memory-consuming. Existing algorithms require separate input and output data stores. However, as the number of watersheds to delineate and the resolution of input data grow significantly, the amount of memory required for an algorithm also quickly increases. MESHED uses one data store for both input and output by destructing input data as processed and a node-skipping depth-first search to further reduce required memory. For 1000 watersheds in Texas, MESHED performed 95% faster than the Central Processing Unit (CPU) benchmark algorithm using 33% less memory. In a scaling experiment, it delineated 100,000 watersheds across the CONUS in 13.64s. Given the same amount of memory, MESHED can solve 50% larger problems than the CPU benchmark algorithm can.

SLO-Net: Enhancing Multiple Sclerosis Diagnosis Beyond Optical Coherence Tomography Using Infrared Reflectance Scanning Laser Ophthalmoscopy Images.

Several machine learning studies have used optical coherence tomography (OCT) for multiple sclerosis (MS) classification with promising outcomes. Infrared reflectance scanning laser ophthalmoscopy (IR-SLO) captures high-resolution fundus images, commonly combined with OCT for fixed B-scan positions. However, no machine learning research has utilized IR-SLO images for automated MS diagnosis. This study utilized a dataset comprised of IR-SLO images and OCT data from Isfahan, Iran, encompassing 32 MS and 70 healthy individuals. A number of convolutional neural networks (CNNs)-namely, VGG-16, VGG-19, ResNet-50, ResNet-101, and a custom architecture-were trained with both IR-SLO images and OCT thickness maps as two separate input datasets. The highest performing models for each modality were then integrated to create a bimodal model that receives the combination of OCT thickness maps and IR-SLO images. Subject-wise data splitting was employed to prevent data leakage among training, validation, and testing sets. Overall, images of the 102 patients from the internal dataset were divided into test, validation, and training subsets. Subsequently, we employed a bootstrapping approach on the training data through iterative sampling with replacement. The performance of the proposed bimodal model was evaluated on the internal test dataset, demonstrating an accuracy of 92.40% ± 4.1% (95% confidence interval [CI], 83.61-98.08), sensitivity of 95.43% ± 5.75% (95% CI, 83.71-100.0), specificity of 92.82% ± 3.72% (95% CI, 81.15-96.77), area under the receiver operating characteristic (AUROC) curve of 96.99% ± 2.99% (95% CI, 86.11-99.78), and area under the precision-recall curve (AUPRC) of 97.27% ± 2.94% (95% CI, 86.83-99.83). Furthermore, to assess the model generalization ability, we examined its performance on an external test dataset following the same bootstrap methodology, achieving promising results, with accuracy of 85.43% ± 0.08% (95% CI, 71.43-100.0), sensitivity of 97.33% ± 0.06% (95% CI, 83.33-100.0), specificity of 84.6% ± 0.10% (95% CI, 71.43-100.0), AUROC curve of 99.67% ± 0.02% (95% CI, 95.63-100.0), and AUPRC of 99.65% ± 0.02% (95% CI, 94.90-100.0). Incorporating both modalities improves the performance of automated diagnosis of MS, showcasing the potential of utilizing IR-SLO as a complementary tool alongside OCT. Should the results of our proposed bimodal model be validated in future work with larger and more diverse datasets, diagnosis of MS based on both OCT and IR-SLO can be reliably integrated into routine clinical practice.

Affordable and real-time antimicrobial resistance prediction from multimodal electronic health records

The spread of antimicrobial resistance (AMR) leads to challenging complications and losses of human lives plus medical resources, with a high expectancy of deterioration in the future if the problem is not controlled. From a machine learning perspective, data-driven models could aid clinicians and microbiologists by anticipating the resistance beforehand. Our study serves as the first attempt to harness deep learning (DL) techniques and the multimodal data available in electronic health records (EHR) for predicting AMR. In this work, we utilize and preprocess the MIMIC-IV database extensively to produce separate structured input sources for time-invariant and time-series data customized to the AMR task. Then, a multimodality fusion approach merges the two modalities with clinical notes to determine resistance based on an antibiotic or a pathogen. To efficiently predict AMR, our approach builds the foundation for deploying multimodal DL techniques in clinical practice, leveraging the existing patient data.

Multilingual non-intrusive binaural intelligibility prediction based on phone classification

Speech intelligibility (SI) prediction models are a valuable tool for the development of speech processing algorithms for hearing aids or consumer electronics. For the use in realistic environments it is desirable that the SI model is non-intrusive (does not require separate input of original and degraded speech, transcripts or a-priori knowledge about the signals) and does a binaural processing of the audio signals. Most of the existing SI models do not fulfill all of these criteria. In this study, we propose an SI model based on phone probabilities obtained from a deep neural net. The model comprises a binaural enhancement stage for prediction of the speech recognition threshold (SRT) in realistic acoustic scenes. In the first part of the study, SRT predictions in different spatial configurations are compared to the results from normal-hearing listeners. On average, our approach produces lower errors and higher correlations compared to three intrusive baseline models. In the second part, we explore if measures relevant in spatial hearing, i.e., the intelligibility level difference (ILD) and the binaural ILD (BILD), can be predicted with our modeling approach. We also investigate if a language mismatch between training and testing the model plays a role when predicting ILD and BILD. This point is especially important for low-resource languages, where not thousands of hours of language material are available for training. Binaural benefits are predicted by our model with an error of 1.5 dB. This is slightly higher than the error with a competitive baseline MBSTOI (1.1 dB), but does not require separate input of original and degraded speech. We also find that good binaural predictions can be obtained with models that are not specifically trained with the target language.

A PINN-DeepONet framework for extracting turbulent combustion closure from multiscalar measurements

In this study, we develop a novel framework to extract turbulent combustion closure, including closure for species chemical source terms, from multiscalar and velocity measurements in turbulent flames. The technique is based on a physics-informed neural network (PINN) that combines models for velocity and scalar measurements and a deep operator network (DeepONet) to accommodate spatial measurements and experimental parameters as separate input streams. An additional key innovation is the estimate of the unconditional means of the species’ chemical source terms as additional “observations” to constrain the prediction of these rates. This estimate is based on a convolution of the means of species reaction rates conditioned on principal components of the multiscalar data and the joint probability density functions of these principal components. The PINN-DeepONet method is implemented on the so-called Sydney flames, where training is carried out on 3 flames and validated on 4 flames. The results show that, despite the limited samples of experimental parameters, including the inlet flow and the fuel jet recess length within the air flow, the PINN-DeepONet approach can construct velocity and scalar fields along with important closure terms for turbulent transport and reaction rates.

Modelling dynamic fracture and fragmentation of rocks under multiaxial coupled static and dynamic loads with a parallelised 3D FDEM

In this study, a three-dimensional (3D) combined finite-discrete element method (FDEM) based simulator, which enables the robust simulation of full-scale triaxial Hopkinson bar (Tri-HB) testing system, including the capture of fracture and fragmentation processes of rock specimens as well as the detection and analysis of generated rock fragments, is developed for investigating the dynamic responses of rocks subjected to multiaxial coupled static and dynamic loads. An innovative two-step approach is proposed to first efficiently achieve the static stress state in the entire Tri-HB system and then apply dynamic loading to the system. The proposed approach with the schemes of mass scaling and local damping can significantly reduce the run time for static computation involving cohesive elements and complex contact mechanics in the framework of explicit time-integration. The developed program is well validated by achieving dynamic stress equilibrium in the Tri-HB system and comparing the simulated stress wave profiles with those from laboratory experiments. Then, the progressive dynamic fracture and fragmentation processes of rocks under different static stress conditions in a full-scale Tri-HB system are investigated, which vividly exhibit the confinement dependence of the dynamic behaviours of rocks in the testing system. It is found that, even with the same loading rate, separate input parameter sets for rock need to be established in FDEM for different confinement conditions to capture the actual rate dependent behaviour of rock. Further studies are also conducted to investigate the effect of interfacial friction on the fracture behaviour, and the results indicate that higher interfacial frictions significantly disturb the dynamic stress variations and the true rock behaviour in the system. Through high performance computing of 3D FDEM, this study is expected to contribute to the understanding of dynamic responses of rocks subjected to multiaxial coupled static and dynamic loads in deep underground engineering.

Sustainability in regulating biotechnology: A new form of knowledge in regulatory co‐production?

AbstractWe analyse the extent to which sustainable development influences the co‐production of regulations that target new technologies in the European Union (EU). We start by identifying the conventional forms of knowledge that serve as inputs into that co‐productive process, namely scientific, societal and legal knowledge. We show that sustainability‐related propositions have gained considerable traction in the regulation of genetic modification (GM) in the EU. Furthermore, the analysis reveals that sustainability cannot be reduced to scientific or societal knowledge. As far as the overlap between sustainability knowledge and legal knowledge is concerned, it is undeniable that sustainable development is deeply embedded into EU regulation; however, treating sustainable development solely as an element of the law does not capture the material influence that it exerts on society and technology, not to speak of its evolutionary flexibility. It follows that it would be best to treat sustainability as a separate input into the co‐creation process.

Synaptic inputs to motor neurons underlying muscle coactivation for functionally different tasks have different spectral characteristics.

The central nervous system (CNS) may produce the same endpoint trajectory or torque profile with different muscle activation patterns. What differentiates these patterns is the presence of cocontraction, which does not contribute to effective torque generation but allows to modulate joints' mechanical stiffness. Although it has been suggested that the generation of force and the modulation of stiffness rely on separate pathways, a characterization of the differences between the synaptic inputs to motor neurons (MNs) underlying these tasks is still missing. In this study, participants coactivated the same pair of upper-limb muscles, i.e., the biceps brachii and the triceps brachii, to perform two functionally different tasks: limb stiffness modulation or endpoint force generation. Spike trains of MNs were identified through decomposition of high-density electromyograms (EMGs) collected from the two muscles. Cross-correlogram showed a higher synchronization between MNs recruited to modulate stiffness, whereas cross-muscle coherence analysis revealed peaks in the β-band, which is commonly ascribed to a cortical origin. These peaks did not appear during the coactivation for force generation, thus suggesting separate cortical inputs for stiffness modulation. Moreover, a within-muscle coherence analysis identified two subsets of MNs that were selectively recruited to generate force or regulate stiffness. This study is the first to highlight different characteristics, and probable different neural origins, of the synaptic inputs driving a pair of muscles under different functional conditions. We suggest that stiffness modulation is driven by cortical inputs that project to a separate set of MNs, supporting the existence of a separate pathway underlying the control of stiffness.NEW & NOTEWORTHY The characterization of the pathways underlying force generation or stiffness modulation are still unknown. In this study, we demonstrated that the common input to motor neurons of antagonist muscles shows a high-frequency component when muscles are coactivated to modulate stiffness but not to generate force. Our results provide novel insights on the neural strategies for the recruitment of multiple muscles by identifying specific spectral characteristics of the synaptic inputs underlying functionally different tasks.

Multi-task learning for seismic elastic parameter inversion with the lateral constraint of angle-gather difference

Pre-stack seismic inversion is an effective way to investigate the characteristics of hydrocarbon-bearing reservoirs. Multi-parameter application is the key to identifying reservoir lithology and fluid in pre-stack inversion. However, multi-parameter inversion may bring coupling effects on the parameters and destabilize the inversion. In addition, the lateral recognition accuracy of geological structures receives great attention. To address these challenges, a multi-task learning network considering the angle-gather difference is proposed in this work. The deep learning network is usually assumed as a black box and it is unclear what it can learn. However, the introduction of angle-gather difference can force the deep learning network to focus on the lateral differences, thus improving the lateral accuracy of the prediction profile. The proposed deep learning network includes input and output blocks. First, angle gathers and the angle-gather difference are fed into two separate input blocks with ResNet architecture and Unet architecture, respectively. Then, three elastic parameters, including P- and S-wave velocities and density, are simultaneously predicted based on the idea of multi-task learning by using three separate output blocks with the same convolutional network layers. Experimental and field data tests demonstrate the effectiveness of the proposed method in improving the prediction accuracy of seismic elastic parameters.

Valuing the manual: the demarcation of embodied practices within algorithmic decision-making processes

ABSTRACT This article takes the notion of the manual as a focal point for understanding the demarcation and evaluation of embodied practices within algorithmic decision-making processes. Focusing on the use of the term manual reveals the limits of automation and the value attached to human intervention. Drawing on extensive qualitative interview data, it is concerned with how those within the UK housing sector approach questions of embodiment and materiality through the lens of the manual. We show how this notion is used to separate human input from algorithmic automation. The article explores how value is attached to the manual and how the term is involved in valuing human interventions. It begins by examining how the manual demarcates spaces and practices outside or alongside the algorithmic. It then focuses upon three manifestations the manual takes within these articulations. First by looking at the manual as the completion of a form, then the manual as a check and finally by looking at the manual as approval. The article develops a central argument concerning processes of valuing and evaluating the manual, that are tied up with these visions and demarcations.

Abstract 2299: More information from limited DNA: simultaneous measurement of genetics, 5hmC and 5mC in cell-free DNA

Abstract Liquid biopsy for profiling of cell free DNA (cfDNA) in blood holds huge promise to transform how we experience and manage cancer by early detection and identification of residual disease and subtype. While early work in liquid biopsy focused on the identification of actionable somatic variations at specific loci, the past decade has seen an expansion into non-genetic features, notably methylation. 5-methylcytosine (5-mC) profiles of cancer are differential from non-cancer at many more loci and so provide a stronger signal. Moreover, recent research has suggested that 5-hydroxymethylcytosine (5-hmC) profiles in cfDNA can be a marker for early cancer. However, a standard blood draw yields an average of only 10ng of cfDNA, presenting the dilemma of how to use limited sample to obtain maximum information. Existing methods for measuring methylation using next-generation sequencing cannot distinguish between 5-mC and 5-hmC and are limited in their ability to detect mutations, with approaches that measure both 5-mC and 5-hmC requiring two separate workflows. Hence, to measure complete genetics, 5-mC and 5-hmC using existing technologies requires three separate workflows, each of which require separate DNA input. We will present a technology which sequences at single base resolution the complete genetic sequence of input DNA fragments integrated with the modification status (unmodified, 5-mC or 5-hmC) for each CpG from low nanogram input quantities of DNA. A hairpin construct is used to create a copy of the original DNA strand. Enzymatic conversion followed by next-generation sequencing enables coupled decoding of bases across the original and copy strand, uniquely reporting one of A, C, G, T, 5-mC, or 5-hmC for each position in the input DNA fragment. Using this technology, we generated whole genome 6-letter data (measuring A, C, G, T, 5-mC, and 5-hmC at single-base resolution across the genome) on cell-free DNA extracted from plasma of healthy volunteers and patients with colorectal cancer at various stages of progression. We demonstrate how the technology can be used to compare 5-mC and 5-hmC methylomic profiles, genomics, and fragmentomics, across different stages of colorectal cancer. In particular, we show how disease progression can be marked by changes in 5-mC and 5-hmC across several loci. We propose that the ability to measure 5-mC and 5-hmC at high accuracy and single base resolution, alongside genomic and fragmentomic profiles, from a limited quantity of DNA will enable greater insight into the ctDNA in plasma and help advance the field of liquid biopsy towards fulfilling its promise. Citation Format: Tom J. Charlesworth, Fabio Puddu, Robert Crawford, Annelie Johansson, Ermira Lleshi, Aurelie Modat, Jamie Scotcher, Michael Wilson, Nicholas Harding, Jean Teyssandier, Jens Fullgrabe, Walraj Gosal, Paidi Creed. More information from limited DNA: simultaneous measurement of genetics, 5hmC and 5mC in cell-free DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2299.

Performance Analysis of Intelligent Computational Algorithms for Biomass Higher Heating Value Prediction

Higher heating value (HHV) is an essential parameter to consider when evaluating and choosing biomass substrates for combustion and power generation. Traditionally, HHV is determined in the laboratory using an adiabatic oxygen bomb calorimeter. Meanwhile, this approach is laborious and cost-intensive. Hence, it is essential to explore other viable options. In this study, two distinct artificial intelligence-based techniques, namely, a support vector machine (SVM) and an artificial neural network (ANN) were employed to develop proximate analysis-based biomass HHV prediction models. The input variables comprising ash, volatile matter, and fixed carbon were paired to form four separate inputs to the prediction models. The overall findings showed that both the ANN and the SVM tools can guarantee accurate prediction in all the input combinations. The optimal prediction performances were observed when fixed carbon and volatile matter were paired as the input combination. This combination showed that the ANN outperformed the SVM, having presented the least root mean squared error of 0.0008 and the highest correlation coefficient of 0.9274. This study, therefore, concluded that the ANN is more preferred compared to SVM for biomass HHV prediction based on the proximate analysis.

Removing input from smell reveals the importance of olfactory input on the sensing of food outside of aroma

Food consumption is by its nature a crossmodal, multisensory experience. The combined inputs from our senses provide the perception of a “flavor”. Altering input from one sensory modality can change the perception from another. Previous research has shown that visual, auditory, olfactory, and somatosensory, as well as gustatory systems contribute to flavor. However, the vast majority of this work concerns adding input to one of the senses, where there is a lack of research into the effect of instead removing passive sensory input from one modality, on the other senses. The study was designed to address the effect of sensory blockade on taste response. Two experiments were performed with this in mind. The first recruited 47 participants rating the sweetness of different solutions when adding or subtracting sensory input. Sucrose, sucrose plus vanilla odor, sucrose plus red color, and sucrose plus both combined were tasted under four conditions: a control, with blindfold, with nose clip, and combined blindfold and nose clip conditions. The results found as expected that adding vanilla odor enhanced the sweetness of the sucrose solution, however a significant inhibitory effect of the nose clips was also plain on sweet taste response. This effect surprisingly did not require a sweet odorant in the sucrose solution; sucrose itself was less sweet without passive olfactory input. No significant effects of visual input were recorded, thus olfaction was focused on in the following test. In the second experiment we investigated olfactory blockade on the other 5 classical senses, to determine if effects observed were limited to links between taste and olfaction, or were more broadly experienced across the senses, and thus could perhaps be attributed to attention or information processing. 45 panelists tested the intensity of sensory stimuli with and without nose clips. Results demonstrated that sweet taste was again impaired by loss of olfactory input, as were umami and bitter (with trends also for sour and salty), chemesthetic stimuli, and of course, detection of aroma. The results of this study suggest a close relationship between olfactory and gustatory input, and that humans may be less able to separate input from the senses, supporting evidence of constant passive crossmodal interaction.

Comparison of soft-computing techniques: Data-driven models for flood forecasting

<p>Accurate flood forecasting is a crucial process for predicting the timing, occurrence, duration, and magnitude of floods in specific zones. This prediction often involves analyzing various hydrological, meteorological, and environmental parameters. In recent years, several soft computing techniques have been widely used for flood forecasting. In this study, flood forecasting for the Narmada River at the Hoshangabad gauging site in Madhya Pradesh, India, was conducted using an Artificial Neural Network (ANN) model, a Fuzzy Logic (FL) model, and an Adaptive Neuro-Fuzzy Inference System (ANFIS) model. To assess their capacity to handle different levels of information, three separate input data sets were used. Our objective was to compare the performance and evaluate the suitability of soft computing data-driven models for flood forecasting. For the development of these models, monthly discharge data spanning 33 years from six gauging sites were selected. Various performance measures, such as regression, root mean square error (RMSE), and percentage deviation, were used to compare and evaluate the performances of the different models. The results indicated that the ANN and ANFIS models performed similarly in some cases. However, the ANFIS model generally predicted much better than the ANN model in most cases. The ANFIS model, developed using the hybrid method, delivered the best performance with an RMSE of 211.97 and a coefficient of regression of 0.96, demonstrating the potential of using these models for flood forecasting. This research highlighted the effectiveness of soft computing techniques in flood forecasting and established useful suitability criteria that can be employed by flood control departments in various countries, regions, and states for accurate flood prognosis.</p>

Micropillar compression using discrete dislocation dynamics and machine learning

Discrete dislocation dynamics (DDD) simulations reveal the evolution of dislocation structures and the interaction of dislocations. This study investigated the compression behavior of single-crystal copper micropillars using few-shot machine learning with data provided by DDD simulations. Two types of features are considered: external features comprising specimen size and loading orientation and internal features involving dislocation source length, Schmid factor, the orientation of the most easily activated dislocations and their distance from the free boundary. The yielding stress and stress-strain curves of single-crystal copper micropillar are predicted well by incorporating both external and internal features of the sample as separate or combined inputs. It is found that the machine learning accuracy predictions for single-crystal micropillar compression can be improved by incorporating easily activated dislocation features with external features. However, the effect of easily activated dislocation on yielding is less important compared to the effects of specimen size and Schmid factor which includes information of orientation but becomes more evident in small-sized micropillars. Overall, incorporating internal features, especially the information of most easily activated dislocations, improves predictive capabilities across diverse sample sizes and orientations.

On polynomially solvable constrained input selections for fixed and switched linear structured systems

This paper investigates two related optimal input selection problems for fixed (non-switched) and switched structured (or structural) systems. More precisely, we consider selecting the minimum cost of inputs from a prior set of inputs, and selecting the inputs of the smallest possible cost with a bound on their cardinality, all to ensure system structural controllability. Those problems have attracted much attention recently; unfortunately, they are NP-hard in general. In this paper, it is found that, if the input structure satisfies certain ’regularizations’, which are characterized by the proposed restricted total unimodularity notion, those problems can be solvable in polynomial time via linear programming (LP) relaxations. Particularly, the obtained characterizations depend only on the incidence matrix relating the inputs and the source strongly connected components (SCC) of the system structure, irrespective of how the inputs actuate states within the same SCC. They cover all the currently known polynomially solvable cases (such as the dedicated input case), and contain many new cases unexplored in the past, among which the source-SCC separated input (SSSI) constraint is highlighted. Further, these results are extended to switched systems, and a polynomially solvable condition, namely the joint SSSI constraint, is obtained that does not require each of the subsystems to satisfy the SSSI constraint. We achieve these by first formulating those problems as equivalent integer linear programmings (ILPs), and then proving the total unimodularity of the corresponding constraint matrices. We also study solutions obtained via LP-relaxation and LP-rounding in the general case, resulting in some lower and upper bounds. Several examples are given to illustrate the obtained theoretical results.

A 1.38-mW 7-bit 1.7-GS/s Single-Channel Loop-Unrolled SAR ADC in 22-nm FD-SOI With 8.85 fJ/Conv.-Step for GHz Mobile Communication and Radar Systems

A 7-bit single-channel loop-unrolled successive approximation register (SAR) analog-to-digital converter (ADC) with a sampling rate of 1.7 GS/s at a power consumption of 1.38 mW is presented. To prove the concept, the circuit was fabricated in a 22-nm fully depleted silicon-on-insulator (FD-SOI) technology. It achieves an effective number of bits (ENOB) of 6.52 and a Walden figure-of-merit () of 8.85 fJ/conv.-step. High sampling speed is achieved by introducing optimized logic, heavily employing dynamic logic for the critical building blocks as the memory cells and clock logic. Sharing of reset transistors between multiple memory cells further reduces the delay between comparator decision and switching of the capacitive digital-to-analog converter (CDAC) by 15%. For calibration of the comparator offset, tuning of the back-gate voltage available in the FD-SOI technology is investigated and employed. Compared with conventional approaches using a separate differential input pair, this reduces the comparator power dissipation by 5% and its noise by 10%. The ADC operates on a single 800-mV supply and uses an active area of 0.0022 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The ADC is the fastest <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;$</tex-math> </inline-formula> 6-bit ENOB single-channel SAR (or pipelined SAR) ADC, while at the same time achieving the best for any previously reported ADC above 1 GS/s.

Pathophysiological discussions and statistical results between diabetes caused by obesity versus cancers caused by obesity via SD-VMT areas comparison using data collected from 2010 to 2023 and based on space-domain viscoplastic energy model of GH-Method: Math-Physical Medicine (No. 893, VGT #293)

The objective of this methodology article is to assess and compare the risks of developing type 2 diabetes (T2D) and cancers caused by obesity as a single input factor. The study spans a timeframe of approximately 14 years, from 2010 to 2023. The author personally collected and analyzed their input data, although there were limitations in obtaining sufficient lab-tested data between 2010 and 2012. From 2013 to 2018, the data collection methods involved more lab testing and personal data obtained through finger-piercing glucose testing. A more complete and comprehensive dataset was obtained from 2018 to 2023, utilizing laboratory tests, automated glucose sensors, and wearable health devices. Despite these limitations, the annual dataset used in this study is considered adequate for drawing useful and reasonably accurate conclusions. The author has been diagnosed with T2D since 1996 but has not received any cancer diagnoses. The assessment of cancer risk in this study is conducted using a sophisticated metabolism index (MI) model developed by the author in 2014. This model takes into account factors such as lifetime bad habits, environmental factors, genetic concerns, four metabolic disorders, and six lifestyle details. To analyze and compare the contribution of T2D (measured through HbA1C levels) and cancer risks based on the single input cause of obesity (measured through body weight, m1), this study utilizes the space-domain viscoplastic energy model from GH-Method: Math-Physical Medicine. In summary, this research presents two key findings: Firstly, the author conducts two separate analyses to evaluate the impact of obesity on his existing T2D condition and his future risks of developing cancer. The SD-VMT analysis shows that the energy level for T2D and obesity is 110.1, while the energy level for cancers and obesity is only 1.18, representing just 1% of the diabetes energy level. This finding indicates that despite being a T2D veteran of 26 years, the author has not shown any signs of developing cancer. Furthermore, his SD time-zone analysis reveals that 98% of the total energy is concentrated in the Y10-Y13 for T2D and Y10-Y17 for cancers, while only 2% falls in the Y14-Y23 for T2D and Y18-Y23 for cancers. Secondly, as an exploratory medical research methodology, he considers obesity as the output symptom, with diabetes and cancers being two separate input causes. The SD-VMT energy ratios demonstrate that diabetes accounts for 69% of the total energy, while cancers account for 31%. Although diabetes has twice as much energy as cancers, the disparity is not as substantial as the 99-fold difference observed in the first case. This finding emphasizes that the author's existing diabetes condition is significantly more severe than his risks of developing cancer.

Drivers' Mental Engagement Analysis Using Multi-Sensor Fusion Approaches Based on Deep Convolutional Neural Networks.

In this paper, we present a comprehensive assessment of individuals' mental engagement states during manual and autonomous driving scenarios using a driving simulator. Our study employed two sensor fusion approaches, combining the data and features of multimodal signals. Participants in our experiment were equipped with Electroencephalogram (EEG), Skin Potential Response (SPR), and Electrocardiogram (ECG) sensors, allowing us to collect their corresponding physiological signals. To facilitate the real-time recording and synchronization of these signals, we developed a custom-designed Graphical User Interface (GUI). The recorded signals were pre-processed to eliminate noise and artifacts. Subsequently, the cleaned data were segmented into 3 s windows and labeled according to the drivers' high or low mental engagement states during manual and autonomous driving. To implement sensor fusion approaches, we utilized two different architectures based on deep Convolutional Neural Networks (ConvNets), specifically utilizing the Braindecode Deep4 ConvNet model. The first architecture consisted of four convolutional layers followed by a dense layer. This model processed the synchronized experimental data as a 2D array input. We also proposed a novel second architecture comprising three branches of the same ConvNet model, each with four convolutional layers, followed by a concatenation layer for integrating the ConvNet branches, and finally, two dense layers. This model received the experimental data from each sensor as a separate 2D array input for each ConvNet branch. Both architectures were evaluated using a Leave-One-Subject-Out (LOSO) cross-validation approach. For both cases, we compared the results obtained when using only EEG signals with the results obtained by adding SPR and ECG signals. In particular, the second fusion approach, using all sensor signals, achieved the highest accuracy score, reaching 82.0%. This outcome demonstrates that our proposed architecture, particularly when integrating EEG, SPR, and ECG signals at the feature level, can effectively discern the mental engagement of drivers.